Credit scoring with a data mining approach based on support vector machines

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Huang:2007:ESA,
  author =       "Cheng-Lung Huang and Mu-Chen Chen and Chieh-Jen Wang",
  title =        "Credit scoring with a data mining approach based on
                 support vector machines",
  journal =      "Expert Systems with Applications",
  year =         "2007",
  volume =       "33",
  number =       "4",
  pages =        "847--856",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Credit
                 scoring, Support vector machine, Neural networks,
                 Decision tree, Data mining, Classification",
  DOI =          "doi:10.1016/j.eswa.2006.07.007",
  abstract =     "The credit card industry has been growing rapidly
                 recently, and thus huge numbers of consumers' credit
                 data are collected by the credit department of the
                 bank. The credit scoring manager often evaluates the
                 consumer's credit with intuitive experience. However,
                 with the support of the credit classification model,
                 the manager can accurately evaluate the applicant's
                 credit score. Support Vector Machine (SVM)
                 classification is currently an active research area and
                 successfully solves classification problems in many
                 domains. This study used three strategies to construct
                 the hybrid SVM-based credit scoring models to evaluate
                 the applicant's credit score from the applicant's input
                 features. Two credit datasets in UCI database are
                 selected as the experimental data to demonstrate the
                 accuracy of the SVM classifier. Compared with neural
                 networks, genetic programming, and decision tree
                 classifiers, the SVM classifier achieved an identical
                 classificatory accuracy with relatively few input
                 features. Additionally, combining genetic algorithms
                 with SVM classifier, the proposed hybrid GA-SVM
                 strategy can simultaneously perform feature selection
                 task and model parameters optimisation. Experimental
                 results show that SVM is a promising addition to the
                 existing data mining methods.",
}

Genetic Programming entries for Cheng-Lung Huang Mu-Chen Chen Chieh-Jen (Steve) Wang

Citations